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Lead Principal Platform Software Engineer, AI Acceleration, Developer Tools

Job in Nashville, Davidson County, Tennessee, 37247, USA
Listing for: Oracle Corporation
Full Time position
Listed on 2026-10-20
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Software Architect, Software Engineer
Salary/Wage Range or Industry Benchmark: 140000 - 210000 USD Yearly USD 140000.00 210000.00 YEAR
Job Description & How to Apply Below

This is an emerging space where the technology, developer expectations, and best practices are changing quickly. We are looking for a technical leader who is energized by that uncertainty: someone who is willing to experiment, prototype, challenge established approaches, learn quickly, and form strong technical opinions based on evidence
. You will explore new capabilities across LLMs, AI agents, reasoning systems, context and retrieval, evaluation, and intelligent automation, while determining which ideas can deliver meaningful and durable improvements to developer productivity.

Success requires more than applying existing AI technologies. You will identify where AI can fundamentally change engineering workflows, develop a technical point of view on where we should invest, and define the architectures and platform capabilities needed to move promising ideas from experimentation to production at OCI scale.

As a Lead Principal Engineer, you will influence the technical direction of AI-powered developer experiences across teams and organizational boundaries
. You will work with senior engineers, engineering leaders, product managers, AI/ML teams, researchers, and OCI service organizations to identify opportunities, test hypotheses, establish architectural direction, and build alignment around consequential technical decisions.

You will remain deeply hands‑on. You will build prototypes, evaluate emerging technologies, design foundational systems, and work alongside engineers to prove what is possible. Equally important, you will know when an experiment has provided enough evidence to invest, pivot, or stop—and how to turn successful experimentation into reliable, secure, scalable platform capabilities.

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